使用 R 的随机森林

Question

i'm working on building a predictive model for breast cancer data using R. After performing gcrma normalization, i generated the potential predictor variables. Now while i run the RF algorithm i encountered the following error

rf_output=randomForest(x=pred.data, y=target, importance = TRUE, ntree = 25001, proximity=TRUE, sampsize=sampsizes)

Error:   Error in randomForest.default(x = pred.data, y = target, importance = TRUE, : Can not handle categorical predictors with more than 53 categories.

code:

library(randomForest)
library(ROCR)
library(Hmisc)
library(genefilter)

setwd("E:/kavya's project_work/final")
datafile<-"trainset_gcrma.txt"
clindatafile<-read.csv("mod clinical_details.csv")

outfile="trainset_RFoutput.txt"
varimp_pdffile="trainset_varImps.pdf"
MDS_pdffile="trainset_MDS.pdf"
ROC_pdffile="trainset_ROC.pdf"
case_pred_outfile="trainset_CasePredictions.txt"
vote_dist_pdffile="trainset_vote_dist.pdf"

data_import=read.table(datafile, header = TRUE, na.strings = "NA", sep="\t")
clin_data_import=clindatafile
clincaldata_order=order(clin_data_import[,"GEO.asscession.number"])
clindata=clin_data_import[clincaldata_order,]
data_order=order(colnames(data_import)[4:length(colnames(data_import))])+3 #Order data without first three columns, then add 3 to get correct index in original file
rawdata=data_import[,c(1:3,data_order)] #grab first three columns, and then remaining columns in order determined above
header=colnames(rawdata)

X=rawdata[,4:length(header)]
ffun=filterfun(pOverA(p = 0.2, A = 100), cv(a = 0.7, b = 10))
filt=genefilter(2^X,ffun)
filt_Data=rawdata[filt,]



#Get potential predictor variables
predictor_data=t(filt_Data[,4:length(header)])
predictor_names=c(as.vector(filt_Data[,3])) #gene symbol
colnames(predictor_data)=predictor_names


target= clindata[,"relapse"]
target[target==0]="NoRelapse"
target[target==1]="Relapse"
target=as.factor(target)

tmp = as.vector(table(target))
num_classes = length(tmp)
min_size = tmp[order(tmp,decreasing=FALSE)[1]]
sampsizes = rep(min_size,num_classes)
rf_output=randomForest(x=pred.data, y=target, importance = TRUE, ntree = 25001, proximity=TRUE, sampsize=sampsizes)


error:"Error in randomForest.default(x = pred.data, y = target, importance = TRUE, : Can not handle categorical predictors with more than 53 categories."

as i'm new to Machine learning i'm unable to proceed. kindly do the needful. Thnks in advance.

Answer 1

It is hard to say without knowing the data. Run class or summary on all your predictor variables to ensure that they are not accidentally interpreted as characters or factors. If you really do have more than 53 levels, you will have to convert them to binary variables. Example:

mtcars$automatic <- mtcars$am == 0
mtcars$manual <- mtcars$am == 1